MS Identification in Brain Magnetic Resonance Images Using Wavelet Transfer Learning

Authors

1 Department of Computer Engineering, Rasht Branch, Islamic Azad University, Rasht, Iran

2 Department of Computer Engineering, Karaj Branch, Islamic Azad University, Karaj, Iran

Abstract

Multiple Sclerosis (MS) is one of the most important diseases of the central nervous system. This disease causes small lesions detectable in Magnetic Resonance Imaging (MRI) images of the patient’s brain. Because of the small size of the lesions, their distribution, and their similarity to some other diseases, the MS diagnosis can be difficult for specialists and may be mistaken. In this paper, we presented a new method based on deep learning for the automatic classification of MRI images. The proposed method is a combinational architecture from transfer learning and wavelet transform (WT). First, WT was applied to the input MRI image, and its four output sub-bands are used as the input of four fine-tuning networks based on EfficientNet-B3. Transfer learning networks perform feature extraction on all four sub-bands. Then, their outputs are combined, and the result is classified by a fully connected neural network. Due to the feature of WT to extract local features, it was possible to highlight the lesions in the images and subsequently classify it with higher accuracy and precision. Various criteria have been used to evaluate the proposed method. The results of the experiments show that the Values of accuracy, precision, sensitivity, and specificity are 98.91%, 99.20%, 99.20%, and 98.33%, respectively.

Keywords


[1] D. S. Reich, C. F. Lucchinetti, and P. A. Calabresi, "Multiple Sclerosis," New England Journal of Medicine, vol. 378, no. 2, pp. 169-180, (2018).
[2] C. H. Polman et al., "Diagnostic criteria for multiple sclerosis: 2010 revisions to the McDonald criteria," (in eng), Annals of neurology, vol. 69, no. 2, pp. 292-302, (2011).
[3] N. N. Sommer et al., "Multiple Sclerosis: Improved Detection of Active Cerebral Lesions With 3-Dimensional T1 Black-Blood Magnetic Resonance Imaging Compared With Conventional 3-Dimensional T1 GRE Imaging," (in eng), Invest Radiol, vol. 53, no. 1, pp. 13-19, Jan (2018).
[4] R. Zivadinov, M. Zorzon, R. De Masi, D. Nasuelli, and G. Cazzato, "Effect of intravenous methylprednisolone on the number, size and confluence of plaques in relapsing–remitting multiple sclerosis," Journal of the Neurological Sciences, vol. 267, no. 1, pp. 28-35, (2008).
[5] S. Jain et al., "Automatic segmentation and volumetry of multiple sclerosis brain lesions from MR images," (in eng), Neuroimage Clin, vol. 8, pp. 367-75, (2015).
[6] J. D. Dworkin et al., "An Automated Statistical Technique for Counting Distinct Multiple Sclerosis Lesions," (in eng), AJNR Am J Neuroradiol, vol. 39, no. 4, pp. 626-633, Apr (2018).
[7] M. Havaei et al., "Brain tumor segmentation with Deep Neural Networks," (in eng), Med Image Anal, vol. 35, pp. 18-31, Jan (2017).
[8] Y.-D. Zhang, Y. Zhang, P. Phillips, Z. Dong, and S. Wang, "Synthetic Minority Oversampling Technique and Fractal Dimension for Identifying Multiple Sclerosis," Fractals, vol. 25, January 01, (2017).
[9] O. Ghribi, L. Sellami, M. Ben Slima, A. Ben Hamida, C. Mhiri, and K. B. Mahfoudh, "An Advanced MRI Multi-Modalities Segmentation Methodology Dedicated to Multiple Sclerosis Lesions Exploration and Differentiation( ",in eng), IEEE Trans Nanobioscience, vol. 16, no. 8, pp. 656-665, Dec (2017).
[10] W. Xueyan and L. Mason, "Multiple Sclerosis Slice Identification by Haar Wavelet Transform and Logistic Regression," in Advances in Materials, Machinery, Electrical Engineering (AMMEE 2017), 2017: Atlantis Press.
[11] Y.-D. Zhang, C. Pan, J. Sun, and C. Tang, "Multiple sclerosis identification by convolutional neural network with dropout and parametric ReLU," Journal of Computational Science, vol. 28, pp. 1-10, 2018/09/01/(2018).
[12] S.-H. Wang et al., "Multiple Sclerosis Identification by 14-Layer Convolutional Neural Network With Batch Normalization, Dropout, and Stochastic Pooling," (in eng), Frontiers in neuroscience, vol. 12, pp. 818-818,(2018).
[13] Z. Ullah, M. Farooq ,S.-H. Lee, and D. An, "A Hybrid Image Enhancement Based Brain MRI Images Classification Technique," Medical Hypotheses, vol. 143, p. 109922, 06/01 (2020).
[14] A. Rezaee, K. Rezaee, J. Haddadnia, and H. T. Gorji, "Supervised meta-heuristic extreme learning machine for multiple sclerosis detection based on multiple feature descriptors in MR images," SN Applied Sciences, vol. 2, no. 5, p. 866, 2020/04/09 (2020).
[15] F. Chollet, Deep Learning with Python. Manning Publications Co., (2017).
[16] K. Weiss and T. Khoshkoftaar, "A Study of the Impact of Base Traditional Learners on Transfer Learning Algorithms," International Journal on Artificial Intelligence Tools, vol. 27, 06/27 (2018).
[17] D. Sarkar, R. Bali, and T. Ghosh, Hands-On Transfer Learning with Python: Implement advanced deep learning and neural network models using TensorFlow and Keras. Packt Publishing, (2018).
[18] K. He, X. Zhang, S. Ren, and J. Sun, "Deep Residual Learning for Image Recognition," in 2016 IEEE Conference on Computer Vision and Pattern
Recognition (CVPR), 2016, pp. 770-778.
[19] C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, "Rethinking the Inception Architecture for Computer Vision," arXiv e-prints, p. arXiv:1512.00567, (2015).
[20] M. Tan and Q. V. Le, "EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks," arXiv e-prints, p. arXiv:1905.11946, (2019).
[21] N. Boussion et al., "A multiresolution image based approach for correction of partial volume effects in emission tomography," Physics in Medicine and Biology, vol. 51, no. 7, pp. 1857-1876, (2006).
[22] S. Jingjing, Y. Ming, X. Bugao, and P. Bel, "Fabric wrinkle characterization and classification using modified wavelet coefficients and support-vector-machine classifiers," Textile Research Journal, vol. 8 2 ,
no. 9, pp. 902-913, 2011/06/01 (2011).
[23] M. Vishwanath, "The recursive pyramid algorithm for the discrete wavelet transform," IEEE Transactions on Signal Processing, vol. 42, no. 3, pp. 673-676, (1994).
[24] N. X. Ríos-Cota and Á. Bernal-Noreña, "Arquitectura hardware para la implementación de la transformada discreta Wavelet 2D," Ingeniería y competitividad, vol. 16, pp. 69-81, (2014).
[25] M. Abadi and e. al., "TensorFlow: A System for Large-Scale Machine Learning," presented at the 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI} 16), Savannah, GA, (2016).
Volume 13, Issue 2
December 2020
Pages 13-21
  • Receive Date: 09 November 2020
  • Revise Date: 05 January 2021
  • Accept Date: 19 January 2021
  • First Publish Date: 19 January 2021